JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE)

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Speech feature extraction based on 2-D independent component analysis

ZOU Xin1,2，LI Wan-long1,LIU Ju1,Peter Jancovic2   

  1. 1. School of Information Science and Engineering,Shandong University;2. School of Electronic,Electrical and Computer Engineering,University of Birmingham,Birmingham,UK B152tt
  • Received:2006-12-21 Revised:1900-01-01 Online:2007-08-24 Published:2007-08-24
  • Contact: ZOU Xin

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2019

Abstract: Independent Component Analysis (ICA) has been widely used in speech signal processing performance. The application of ICA to speech feature extraction is investigated. Normally the ICA algorithm is applied in the logarithm Mel-scaled filter bank domain to replace the discrete cosine transform (DCT), which is applied to obtain Mel-Frequency Cepstral Coefficients (MFCCs). A novel 2-D ICA algorithm is proposed, with which one can explore the spectral and temporal information of speech signals, and improve the effectiveness and noise robustness of speech features. The evaluation is presented for GMM-based speaker recognition task. The experimental results show that the proposed 2-D features provide  better performance than normal 1-D features.

Key words: ICA, speech feature extraction, speaker identification

CLC Number: 

  • TN912.34
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